Streamlining support dialogues via transitive relationships between different dialogues

ABSTRACT

A computer-implemented method includes storing, by a computing device, a plurality of dialogues between user devices and an automated support application hosted by the computing device; determining, by the computing device, transitive relationships between the plurality of dialogues; and updating, by the computing device, a question mapping based on the determining the transitive relationships; and applying, the computing device, the updated question mapping to a subsequent support dialogue.

BACKGROUND

The present invention generally relates to streamlining supportdialogues and, more particularly, to streamlining support dialogues viatransitive relationships between different dialogues.

A user may interact with a virtual support agent (e.g., a chat bot,automated support agent, or the like) to obtain information orassistance for a technical issue (e.g., to reset a password, makechanges to a service account, etc.). A virtual support agent (referredto as an “agent”) and a user may engage in a dialogue in which the agentasks questions to determine the user's issues and provide the user witha solution. The user's intent when providing responses to an agent'squestions can be determined using natural language processing based on a“first phrase” included in the user's first responses to agentquestions, but may change throughout the dialogue.

SUMMARY

In an aspect of the invention, a computer-implemented method includesstoring, by a computing device, a plurality of dialogues between userdevices and an automated support application hosted by the computingdevice; determining, by the computing device, transitive relationshipsbetween the plurality of dialogues; and updating, by the computingdevice, a question mapping based on the determining the transitiverelationships; and applying, the computing device, the updated questionmapping to a subsequent support dialogue.

In an aspect of the invention, there is a computer program product forstreamlining support dialogues, the computer program product comprisinga computer readable storage medium having program instructions embodiedtherewith. The program instructions are executable by a computing deviceto cause the computing device to: receive, via an automated supportapplication hosted by the computing device, an initial user input from auser via a user device; provide one or more follow-up questions based onreceiving the initial user input and a question mapping; receive one ormore follow-up responses to the one or more follow-up questions; store adialogue, wherein the dialogue includes the initial user input, the oneor more follow-up questions, and the one or more follow-up responses;determine a transitive relationship between the stored dialogue and onemore previously stored dialogues; update the question mapping based onthe determining the transitive relationships; and apply the updatedquestion mapping to a subsequent support dialogue.

In an aspect of the invention, a system includes: a CPU, a computerreadable memory and a computer readable storage medium associated with acomputing device; program instructions to store a plurality of dialoguesbetween user devices and an automated support application hosted by thecomputing device; program instructions to determine transitiverelationships between the plurality of dialogues; and programinstructions to merge and abridge text transcripts of a subset of theplurality of dialogues that are transiently related to form an abridgedtext transcript; and program instructions to apply the abridged texttranscript to a subsequent support dialogue. The program instructionsare stored on the computer readable storage medium for execution by theCPU via the computer readable memory.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in the detailed description whichfollows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention.

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIGS. 4A and 4B show an overview of an example implementation inaccordance with aspects of the present invention.

FIG. 5 shows an example environment in accordance with aspects of thepresent invention.

FIG. 6 shows a block diagram of example components of an agent device inaccordance with aspects of the present invention.

FIGS. 7A and 7B show an example flowchart for determining transitiverelationships between dialogues to streamline future dialogues inaccordance with aspects of the present invention.

DETAILED DESCRIPTION

The present invention generally relates to streamlining supportdialogues and, more particularly, to streamlining support dialogues viatransitive relationships between different dialogues. A virtual supportagent (referred to as an “agent”) and a user may engage in a dialogue inwhich the agent asks questions to determine the user's support issueswith an application, or other computer-related system, and provide theuser with a solution. The user's intent when providing responses to anagent's questions can be determined using natural language processingbased on a “first phrase” included in the user's first responses toagent questions, but may change throughout the dialogue. Also, the usermay not know the specific issue with which he or she needs assistance,and thus may engage in a “back and forth” with the agent to arrive at asolution. Further, the agent may misclassify the dialogue based on aninitial classification that is determined from an initial user utterance(e.g., “first phrase”). When an entire dialogue is misclassified, theagent may not provide the needed information to assist the user inresolving an issue, or may end up asking the user a series of extraneousquestions that waste the user's time in seeking resolution to the issue.

Accordingly, aspects of the present invention may track transitiverelationships between different dialogues between users and agents, anduse the transitive relationships to streamline future dialogues betweenusers and agents and better identify the user's intent based on theirresponses to agent questions. More specifically, aspects of the presentinvention may eliminate sections or lines of questions of a dialoguethat do not lead to resolving the user's issue. In embodiments, aspectsof the present invention may “match” or compare dialogues that aresimilar, and may use these comparisons to determine transitiverelationships between the dialogues. For example, each agent query anduser response may be a “node” in a dialogue graph, and comparison ofdialogue graphs and transcripts may be used to identify transitiverelationships between the nodes in the dialogue. As such, the shortest“path” from one node (e.g., a first phrase or first response to an agentquery) to a final node (e.g., a resolution of an issue) can bedetermined. In this way, the amount of time spent with an agent and thenumber of questions presented and answered is minimized. Further, theintent of the user for a new dialogue is more accurately determinedbased on comparing historical similar dialogues to identify thetransitive relationship between a new dialogue and historical dialogues.

Aspects of the present invention may also generate and present dialoguemaps/graphs for informational purposes and/or metrics tracking. Thedialogue maps may also be used to determine transitive relationshipsbetween dialogues, and may also be used to analyze the operations andeffectiveness of an agent. For example, if a dialogue map includes arelatively large number of questions and reclassifications, adetermination can be made that the dialogue was relatively ineffectiveand convoluted so that adjustments to the agent's question mappings canbe adjusted. In embodiments, the dialogue map may identify agentquestions, user responses, and classifications of the user responses(e.g., as determined using natural language classification/processing).In this way, comparison of multiple dialogue maps may be used toidentify the “shortest path” between a particular initial userutterance, and a final classification/intent of the user based on theinitial utterance.

Aspects of the present invention may improve the functioning ofcomputer-based automated support agents by reducing the computingresources used by automated support applications hosted by agentservers. For example, since the number of questions and overall lengthof dialogues is reduced using the systems and/or methods describedherein, the amount of computing resources consumed for resolving supportissues is reduced. Further, computing capacity for other applications orto serve additional users is increased since the computing resourcesused for each support dialogue is reduced. In turn, the speed,performance, and capacity of computing resources are improved. Further,aspects of the present invention may collect hundreds or potentiallythousands of dialogues over a period of time to update, streamline, andshorten the length of interactions between a user and an agent devicefor resolving a support issue. As such, aspects of the present inventioncannot be done with pen and paper. Further, aspects of the presentinvention produce a specific result of reducing the length of a dialogueand improving dialogue accuracy, leading to an improvement in customersatisfaction, and reducing costs for service providers when providingsupport services to customers. In other words, aspects of the presentinvention update automated chat algorithms, processes, and question mapswith the specific purpose, benefit, and result of reducing the length ofa dialogue and improving dialogue accuracy, rather than merelydisplaying results of analyzed data.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a nonremovable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and dialogue intent determination andstreamlining 96.

Referring back to FIG. 1, the program/utility 40 may include one or moreprogram modules 42 that generally carry out the functions and/ormethodologies of embodiments of the invention as described herein (e.g.,such as the functionality provided by dialogue intent determination andstreamlining 96). Specifically, the program modules 42 may receive auser query as part of a dialogue with an agent, determine aclassification for the query, provide follow-up questions to confirm theclassification, score responses to the follow-up questions, compare thedialogue with other dialogues to determine transitive relationships, andrefine/update question mappings based on the transitive relationships tostreamline future dialogues between users and the agent. Otherfunctionalities of the program modules 42 are described further hereinsuch that the program modules 42 are not limited to the functionsdescribed above. Moreover, it is noted that some of the modules 42 canbe implemented within the infrastructure shown in FIGS. 1-3. Forexample, the modules 42 may be representative of an agent device asshown in FIG. 4A.

FIGS. 4A and 4B shows an overview of an example implementation inaccordance with aspects of the present invention. In FIG. 4A, an agentdevice 220 may include one or more computing devices (e.g., servers)that provide automated support assistance to users through user devices210. For example, a user may use a user device 210 to access anautomated support application or service hosted by the agent device 220.The user device 210 and the agent device 220 may conduct a “dialogue” inwhich the user device 210 provides user inputs relating to obtainingsupport for a technical issue (e.g., to reset a password, obtaintechnical information for a product or application, troubleshoot aproduct or application, etc.). The agent device 220 may use naturallanguage processing to recognize user inputs made by the user andreceived from user device 210, ask a series of follow-up questions tobetter classify and/or determine the intent of the user's inputs, andprovide the user with a resolution to their support issue based ondetermining the intent of the user's inputs.

While certain support issues may seem relatively simple (e.g., apassword reset request), in practice, the support issue may be morecomplex, leading to the need for a relatively long dialogue between theuser device 210 and the agent device 220 to arrive at solution to auser's support issue. For example, a password reset issue could relateto a multitude of possible support issues, such as troubleauthenticating past a hard-drive encryption log-in screen for acomputer, authenticating past a log-in screen for an operating system,authenticating to access a secure network drive or database,authenticating to access an application, etc. Thus, if a user providesuser input to the agent device 220 indicating that the user is havingpassword issues, the specific issue may not be known based on the user'sinitial query, “first phrase” or “utterance.” Accordingly, the agentdevice 220 may ask a series of follow-up questions to properly classifythe dialogue and provide resolution.

During a dialogue, the agent device 220 may use a question map to betteridentify the user's specific support issue and provide properresolution. For example, the agent device 220 may ask a series offollow-up questions based on user inputs, in which the follow-upquestions are based on a question map. The agent device 220 may classifythe dialogue such that information for resolving the user's issue can beprovided to the user device 210 based on the classification. If adialogue is not initially classified correctly (e.g., based on userinput indicating that information provided to the user does not resolvethe user's issue), additional follow-up questions may be asked by theagent device 220 so that the dialogue may be reclassified. In certainsituations, a dialogue may be reclassified multiple times in the eventthat the agent device 220 is unable to initially classify a dialoguecorrectly, and the dialogue may be reclassified until the correctclassification is confirmed by the user.

Accordingly, and as shown in FIG. 4A, aspects of the present inventionmay track dialogues with multiple different users and user devices 210over a period of time. More specifically, the agent device 220 may trackuser inputs/queries, agent responses to those queries, classificationsmade throughout the dialogues, user responses to queries made by theagent device 220, etc., to determine transitive relationships betweenthe dialogues. Further, the agent device 220 may update questionmappings to streamline future dialogues based on the transitiverelationships. For example, the agent device 220 may determine intentbehind certain user inputs, questions, and responses by determiningtransitive relationships between different dialogues. The transitiverelationships may then be used to update question mappings and determinethe relevant steps and questions asked by the agent device 220 to arriveat a resolution to the user's support issue, while eliminating thenon-relevant questions that do not lead to a resolution. In this way,future dialogues may be streamlined and shortened by leveraging thedetermined transitive relationships, and the accuracy of dialogueclassification may be improved. As further shown in FIG. 4A, the agentdevice 220 may optionally output dialogue graphs and reports 310 thatvisually display a dialogue in a linear graph format.

In embodiments, the agent device 220 may generate linear dialogue graphsof each dialogue in order to determine the transitive relationships.With reference to FIG. 4B, each dialogue graph may identify “nodes” inwhich each node is a communication from the agent device 220 (denoted by“A”) and from the user (denoted by “U”). The dialogue graph may show thepath of the dialogue from start to finish, as well as classificationsmade at various nodes throughout the dialogue (denoted by “C1” C3”, and“C4”). For example, as described above, the dialogue may be reclassifiedmultiple times until a correct classification is confirmed by the user.In the example shown in FIG. 4B, the dialogue may be classified as “C1”,reclassified to “C3”, and reclassified again to “C4” (e.g., when theuser indicates that the support issue is not related to “C1” or “C3”,but confirms that the support issue is related to “C4”).

The path may include a series of tangential questions that are asked bythe agent device 220 and responded to by the user via the user device210. The series of questions asked by the agent device 220 may be basedon a question map that maps questions to user responses as determined bynatural language processing. While not shown in FIG. 4B, the dialoguegraphs may be associated with transcripts of the dialogue (e.g., userconversation inputs, agent responses, etc.). As described herein, thedialogue graph and transcripts may be compared and matched with othersimilar dialogues (e.g. dialogues in which a same classification waspresent at some point during the dialogue). By matching the dialogues,transitive relationships between the dialogues (and more specifically,between different nodes, questions, and responses within the dialogue)can be determined. As further shown in FIG. 4B, an updated dialoguegraph may be generated in which the series of questions have beenreduced to more quickly arrive at a correct classification, and hence, aresolution, based on determining the transitive relationships frommatching the dialogues. Further, the accuracy of dialogue classificationmay be improved.

As an illustrative, non-limiting example, a user may use a user device210 to access a support application or service hosted by the agentdevice 220, and provide an initial phrase or utterance of: “I am lockedout and need password assistance.” The agent device 220 may applynatural language processing and initially classify the user input as apassword issue. The agent device 220 may access a question map thatidentifies follow-up questions to ask based on the processed user inputand/or classification. As an example, the agent device 220 may askfollow-up questions that identify more specifically which system inwhich the user is locked out. For example, the agent device 220 may aska series of follow-up questions to determine whether the user is lockedout of an e-mail system, share drive system, virtual private network(VPN) system, hard drive encryption screen, etc. The agent device 220may score the user's responses to determine which follow-up questionsare considered relevant (e.g., lead to a resolution of the user'sissue). The dialogue may be graphed in the manner discussed above, andsaved for future comparisons with other dialogues.

As other similar dialogues are saved with other users, the agent device220 may compare these dialogues and determine transitive relationshipsbetween the dialogues to shorten or streamline the line of questionsneeded to arrive at the solution to the user's issue. In the aboveexample, the agent device 220 may determine, over a period of time fromsaving and tracking multiple different dialogues, that when a userinitially asks for password assistance, the user is likely asking forpassword assistance relating to accessing a company e-mail account.Accordingly, the agent device 220 may update the question map so thatfewer follow-up questions are needed to arrive at a solution for theuser. It is emphasized that the above is merely an exampleimplementation, and aspects of the present invention may be applied toany number of situations or support issues in which transitiverelationships between dialogues can be determined to streamline futuredialogues and improve classification accuracy.

FIG. 5 shows an example environment in accordance with aspects of thepresent invention. As shown in FIG. 5, environment 500 may include userdevices 210, an agent device 220, external data servers 230, and anetwork 240. In embodiments, one or more components in environment 500may correspond to one or more components in the cloud computingenvironment of FIG. 2. In embodiments, one or more components inenvironment 500 may include the components of computer system/server 12of FIG. 1.

A user device 210 may include a device capable of communicating via anetwork, such as the network 240. For example, the user device 210 maycorrespond to a mobile communication device (e.g., a smart phone or apersonal digital assistant (PDA)), a portable computer device (e.g., alaptop or a tablet computer), a desktop computer, or another type ofdevice. In some embodiments, the user device 210 may be used to accessan automated support application or service hosted by the agent device220. The user device 210 may include a user interface to receive userinputs to the agent device 220 and receive responses from the agentdevice 220 as part of a support dialogue.

The agent device 220 may include one or more computing or server devices(e.g., such as computer system/server 12 of FIG. 1) that host anautomated support application or service accessible by the user device210. The agent device 220 may receive user inputs from the user device210 that identify the user and relate to a support issue with which theuser is seeking assistance. The agent device 220 may host a dialoguewith the user via the user device 210, apply natural language processingto determine the intent of the dialogue, ask follow-up questions basedon a question map, classify the dialogue, generate a dialogue graph,store the dialogue, compare the dialogue with other dialogues todetermine transitive relationships, and streamline or update thequestion map to shorten or streamline future dialogues and improve theaccuracy of dialogue classification. In embodiments, the agent device220 may communicate with one or more external data servers 230 to obtainuser information and/or other information (e.g., from web sources usingsearching techniques) that may be used to assist the user resolving asupport issue.

The external data servers 230 may include one or more computing orserver devices (e.g., such as computer system/server 12 of FIG. 1) thatstore information that may be used to assist the user resolving asupport issue. For example, the external data servers 230 may includeuser or employee information databases that identify user accounts,services, applications, domains, etc. that a user may have permission toaccess. As an illustrative example, the agent device 220 may query theexternal data server 230 to determine domains or directories that theuser has permission to access, as this this information may be pertinentwhen resolving a user support issue. Additionally, or alternatively, theexternal data servers 230 may include web servers that host any varietyof webpages, applications, social media platforms, or the like, whichmay be accessed to provide the user with any variety of information toresolve the user's support issue.

The network 240 may include network nodes, such as network nodes 10 ofFIG. 2. Additionally, or alternatively, the network 240 may include oneor more wired and/or wireless networks. For example, the network 240 mayinclude a cellular network (e.g., a second generation (2G) network, athird generation (3G) network, a fourth generation (4G) network, a fifthgeneration (5G) network, a long-term evolution (LTE) network, a globalsystem for mobile (GSM) network, a code division multiple access (CDMA)network, an evolution-data optimized (EVDO) network, or the like), apublic land mobile network (PLMN), and/or another network. Additionally,or alternatively, the network 240 may include a local area network(LAN), a wide area network (WAN), a metropolitan network (MAN), thePublic Switched Telephone Network (PSTN), an ad hoc network, a managedInternet Protocol (IP) network, a virtual private network (VPN), anintranet, the Internet, a fiber optic-based network, and/or acombination of these or other types of networks.

The quantity of devices and/or networks in the environment 500 is notlimited to what is shown in FIG. 5. In practice, the environment 500 mayinclude additional devices and/or networks; fewer devices and/ornetworks; different devices and/or networks; or differently arrangeddevices and/or networks than illustrated in FIG. 5. Also, in someimplementations, one or more of the devices of the environment 500 mayperform one or more functions described as being performed by anotherone or more of the devices of the environment 500. Devices of theenvironment 500 may interconnect via wired connections, wirelessconnections, or a combination of wired and wireless connections.

FIG. 6 shows a block diagram of example components of an agent device220 in accordance with aspects of the present invention. As shown inFIG. 6, the agent device 220 may include a user input receiving module610, a natural language processing module 620, a classification module630, a question mapping repository 640, a dialogue repository 650, aresponse scoring module 660, a dialogue graphing module 670, atransitive relationship determination module 680, and a question mappingupdating module 690. In embodiments, the agent device 220 may includeadditional or fewer components than those shown in FIG. 6. Inembodiments, separate components may be integrated into a singlecomputing component or module. Additionally, or alternatively, a singlecomponent may be implemented as multiple computing components ormodules.

The user input receiving module 610 may include a program module (e.g.,program module 42 of FIG. 1) that receives user input from the userdevice 210. More specifically, the user input receiving module 610 mayreceive user input via an automated support application hosted by theagent device 220. The user input may include an initial request forsupport assistance, and responses to follow-up questions presented bythe agent device 220.

The natural language processing module 620 may include a program module(e.g., program module 42 of FIG. 1) that applies natural languageprocessing techniques to user input received via the user inputreceiving module 610. In embodiments, the natural language processingmodule 620 may apply any suitable computer-based natural languageprocessing technique which may be used to automatically classify and/ordetermine intent of the user input (e.g., as described below withrespect to the classification module 630).

The classification module 630 may include a program module (e.g.,program module 42 of FIG. 1) that classifies user inputs. Inembodiments, the classification module 630 may determine an initialclassification based on the user's first phrase or utterance. Theclassification module 630 may reclassify a dialogue throughout theduration of the dialogue. The classification module 630 may classify orreclassify user inputs based on natural language processing performed bythe natural language processing module 620. In embodiments, theclassification module 630 may apply sub-classifications to the userinputs to better identify the user's intent. If a dialogue cannot beclassified, the classification module 630 may provide an indication thatthe user input cannot be classified such that follow-up questions can beasked in order to classify the dialogue. In embodiments, theclassification module 630 may confirm that a classification is correctbased on user responses indicating that the classification is correct.For example, the classification module 630 may output informationindicating the classification or intent behind the dialogue, and theuser may provide user input to confirm that the classification iscorrect.

The question mapping repository 640 may include a data storage device(e.g., storage system 34 of FIG. 1) that stores a data structure thatmaps follow-up questions to ask a user based on the user's inputs andclassification of those inputs (e.g., the classifications as determinedby the classification module 630). For example, the follow-up questionsmay include questions that may confirm the classification, and/orquestions that ask more specific questions to better classify orsub-classify the dialogue. As described herein, the question maps storedby the question mapping repository 640 may be updated to removenon-relevant question paths.

The dialogue repository 650 may include a data storage device (e.g.,storage system 34 of FIG. 1) that stores dialogues between users and theagent device 220 through the automated support application. Inembodiments, the dialogue repository 650 may store chat or texttranscripts and/or dialogue graphs for each dialogue, which may be usedto match dialogues to determine transitive relationships, as describedherein.

The response scoring module 660 may include a program module (e.g.,program module 42 of FIG. 1) that scores user responses and inputs toquestions presented by the agent device 220. In embodiments, theresponse scoring module 660 may score user responses (e.g., received bythe user input receiving module 610) based on level of relevance towhich the responses lead to a resolution to a user support issue. Forexample, if a user response to a question leads to a correctclassification or sub-classification of the dialogue, that user responsemay be scored relatively highly, whereas if a user response to aquestion does not lead to a correct classification or sub-classificationof the dialogue, at user response may be scored relatively low. Asdescribed herein, the scores may be used to prune question mappingsand/or question lists and reduce the number of questions andinteractions needed to arrive at a correct classification and solutionto a user's issue.

The dialogue graphing module 670 may include a program module (e.g.,program module 42 of FIG. 1) that graphs a dialogue. An example of adialogue graph is shown in FIG. 4B. As described herein, the dialoguerepository 650 may store dialogue graphs, and the dialogue graphs may beused to determine transitive relationships between dialogues (e.g., bythe transitive relationship determination module 680 as describedherein).

The transitive relationship determination module 680 may include aprogram module (e.g., program module 42 of FIG. 1) that may comparestored dialogues with each other (e.g., dialogues stored by the dialoguerepository 650) to determine transitive relationships between thedialogues, and more specifically, determine transitive relationshipsbetween specific nodes within the dialogue graphs (e.g., as generated bythe dialogue graphing module 670). Further, the transitive relationshipdetermination module 680 may determine transitive relationships based oncommonalities between text transcripts of different dialogues. Inembodiments, the transitive relationship determination module 680 maydetermine that one node in a dialogue graph (e.g., a first phrase, orone of the first phrases in a dialogue) is transitively related to aparticular solution (e.g., a later node in the dialogue graph). Inembodiments, comparison of the dialogue graph may prevent situations inwhich similar dialogues are incorrectly matched such that only dialoguesthat are similar to a particular degree are matched for determiningtransitive relationships. For example, dialogue graphs that have similarending classifications and outcomes may be matched, and those dialoguegraphs may be compared to identify correct and accurate transitiverelationships. In turn, the “shortest path” between nodes (e.g., theshortest dialogue to reach a resolution) can be determined.

The question mapping updating module 690 may include a program module(e.g., program module 42 of FIG. 1) that updates question mappingsstored by the question mapping repository 640. In embodiments, thequestion mapping updating module 690 may update the question mappingsbased on the transitive relationships determined by the transitiverelationship determination module 680. In embodiments, the questionmapping updating module 690 may eliminate nodes (e.g., questions) in adialogue graph such that the path between an initial utterance and afinal resolution is shortened. In other words, the question mappingupdating module 690 may eliminate non-relevant lines of questions thatdo not lead to a solution to the user's support issue. Further, thequestion mapping updating module 690 may eliminate non-relevant lines ofquestions based on the scores to user responses as determined by theresponse scoring module 660. Additionally, or alternatively, thequestion mapping updating module 690 may abridge merged text transcriptsof transitively related dialogues to include only the relevant portionsof text that lead to a solution to the user's support issue. Forexample, the question mapping updating module 690 may generate anabridged text transcript that includes the text from the transitivelyrelated dialogues but with the non-relevant portions from eachtransitively related dialogue excised or removed. In embodiments, theabridged text transcript may be used as a “road map” or guide for futuredialogues such that future dialogues only include relevant questionsthat lead to a solution to a user's support issue.

FIGS. 7A and 7B show an example flowchart of a process for determiningtransitive relationships between dialogues to streamline futuredialogues. The steps of FIG. 7A and 7B may be implemented in theenvironment of FIG. 5, for example, and are described using referencenumbers of elements depicted in FIGS. 5 and 6. As noted above, theflowchart illustrates the architecture, functionality, and operation ofpossible implementations of systems, methods, and computer programproducts according to various embodiments of the present invention.

As shown in FIG. 7A, process 700 may include receiving user input via anautomated support application and begin generating a dialogue graphthrough the dialogue (step 705). For example, as described above withrespect to the user input receiving module 610, the agent device 220 mayuser input from the user device 210. More specifically, the agent device220 may receive user input via an automated support application hostedby the agent device 220. The user input may include a request forsupport to resolve a support issue. Throughout process 700, the agentdevice 220 may generate and update a dialogue graph (e.g., using thedialogue graphing module 670) as the dialogue between the user via theuser device 210 and the agent device 220 progresses with questions,answers, and classification determinations as described herein.

Process 700 may also include determining whether the user input can beclassified (step 710). For example, as described above with respect tothe natural language processing module 620 and the classification module630, the agent device 220 may apply natural language processing to theuser input to classify the user input.

If, for example, the user input cannot be classified (step 710-NO),process 700 may further include determining and providing follow-upquestions (step 715). For example, as described above with respect tothe question mapping repository 640, the agent device 220 may determinefollow-up questions based on a question map stored by the questionmapping repository 640. The follow-up question may be based on naturallanguage processing and a “best guess” of a classification or intent ofthe user's input, as well as the words included in the user input. Thefollow-up question may be provided in order to determine theclassification for the dialogue.

Process 700 may also include receiving follow-up responses (step 720).For example, as described above with respect to the user input receivingmodule 610, the agent device 220 may receive responses from the userdevice 210 to the follow-up questions. Process 700 may return to step710 where the agent device 220 attempts to classify the dialogue basedon the response to the follow up questions.

If, at step 710, a classification is determined (step 710-YES), process700 may further include providing follow-up questions to confirm theclassification (step 725). For example, as described above with respectto the question mapping repository 640, the agent device 220 maydetermine follow-up questions based on determining the classification.The follow-up questions may ask the user to confirm the classification,or may include a resolution to the user's support issue with a requestto conform that the resolution has resolved the user's support issue.

Process 700 may also include receive a follow-up response (step 730).For example, as described above with respect to the user input receivingmodule 610, the agent device 220 may receive a response from the userdevice 210 to the follow-up question.

Process 700 may further include determining whether the classificationhas been confirmed, (step 735). For example, as described above withrespect to the classification module 630, the agent device 220 mayconfirm whether the classification is correct based on the user responseto the follow-up question.

If, for example, the classification is not confirmed (step 735-NO),process 700 may return to step 715 where follow-up questions aredetermined to re-classify the dialogue. As described herein, agentdevice 220 may store the classification at a corresponding node in thedialogue graph as the dialogue is classified and reclassified.

If, on the other hand, the classification is confirmed (step 735-YES),and as shown in FIG. 7B, process 700 may include storing the dialoguewith the dialogue graph and transcript (step 740). For example, asdescribed above with respect to the dialogue repository 650, the agentdevice 220 may store the dialogue transcript and graph in the dialoguerepository 650.

Process 700 may further include scoring the user responses within thedialogue (step 745). For example, as described above with respect to theresponse scoring module 660, the agent device 220 may score the userresponses based on level of relevance to which the responses lead to aresolution to a user support issue. In embodiments, the agent device 220may score the user responses by analyzing the dialogue graph and thepaths within the dialogue graph to determine which user responses leadto a correct classification of the dialogue.

Process 700 may further include comparing the dialogue with other storeddialogues to determine transitive relationships (step 750). For example,as described above with respect to the transitive relationshipdetermination module 680, the agent device 220 may compare storeddialogues with each other to determine transitive relationships betweenthe dialogues, and more specifically, determines transitiverelationships between specific nodes within the dialogues. Further, theagent device 220 may determine transitive relationships based onabridging text transcripts of different dialogues. In embodiments, thetransitive relationship determination module 680 may determine that onenode in a dialogue graph (e.g., a first phrase, or one of the firstphrases in a dialogue) is transitively related to a particular solution(e.g., a later node in the dialogue graph).

Process 700 may also include updating question mappings and/or abridgetext transcripts based on the transitive relationships and/or scores(step 755). For example, as described above with respect to the questionmapping updating module 690, the agent device 220 may update questionmappings stored by the question mapping repository 640. In embodiments,the agent device 220 may update the question mappings based on thetransitive relationships determined by the transitive relationshipdetermination module 680. In embodiments, the agent device 220 mayeliminate nodes (e.g., questions) in a dialogue graph such that the pathbetween an initial utterance and a final resolution is shortened. Inother words, the agent device 220 may eliminate non-relevant lines ofquestions that do not lead to a solution to the user's support issue.Further, the agent device 220 may eliminate non-relevant lines ofquestions based on the scores to user responses as determined by theresponse scoring module 660. Additionally, or alternatively, the agentdevice 220 may abridge text transcripts of transitively relateddialogues to include only the relevant portions of text that lead to asolution to the user's support issue. In embodiments, the abridged texttranscripts may be used as a “road map” or guide for future dialoguessuch that future dialogues only include relevant questions that lead toa solution to a user's support issue.

Process 700 may further include applying the updated question mappingand/or abridged text transcript to subsequent dialogues (step 760). Forexample, the updated question mappings and/or the abridged texttranscript may be applied to future or subsequent support dialogues suchthat the future or subsequent support dialogues having similar userresponses are shortened and streamlined to arrive at a solution to auser's support issue more quickly, and to more accurately classify thedialogue.

In embodiments, a service provider could offer to perform the processesdescribed herein. In this case, the service provider can create,maintain, deploy, support, etc., the computer infrastructure thatperforms the process steps of the invention for one or more customers.These customers may be, for example, any business that uses technology.In return, the service provider can receive payment from the customer(s)under a subscription and/or fee agreement and/or the service providercan receive payment from the sale of advertising content to one or morethird parties.

In still additional embodiments, the invention provides acomputer-implemented method, via a network. In this case, a computerinfrastructure, such as computer system/server 12 (FIG. 1), can beprovided and one or more systems for performing the processes of theinvention can be obtained (e.g., created, purchased, used, modified,etc.) and deployed to the computer infrastructure. To this extent, thedeployment of a system can comprise one or more of: (1) installingprogram code on a computing device, such as computer system/server 12(as shown in FIG. 1), from a computer-readable medium; (2) adding one ormore computing devices to the computer infrastructure; and (3)incorporating and/or modifying one or more existing systems of thecomputer infrastructure to enable the computer infrastructure to performthe processes of the invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:storing, by a computing device, a plurality of dialogues between userdevices and an automated support application hosted by the computingdevice; applying, by the computing device, natural language processingto automatically determine one or more classifications of the dialoguesat different points in the dialogues; determining, by the computingdevice, transitive relationships between the plurality of dialogues; andupdating, by the computing device, a question mapping based on thedetermining the transitive relationships; and applying, by the computingdevice, the updated question mapping to a subsequent support dialogue,wherein the storing the plurality of dialogues further includes storingthe one or more classifications.
 2. The method of claim 1, wherein theupdating the question mapping reduces a number of questions included inthe question mapping.
 3. The method of claim 1, wherein the storing theplurality of dialogues includes storing respective dialogue graphsassociated with each of the plurality of dialogues.
 4. The method ofclaim 3, wherein storing a dialogue graph of the respective dialoggraphs comprises generating the dialogue graph, wherein the generatingthe dialogue graph comprises generating nodes representing responses byeach of the user device and the automated support application and a pathbetween the responses.
 5. The method of claim 4, wherein the one or moreclassifications are determined at each node in the graph.
 6. The methodof claim 4, wherein the nodes in the dialogue graph represent follow-upquestions presented by the automated support application and responsesto the follow-up questions.
 7. The method of claim 3, wherein thedetermining the transitive relationships includes matching a subset ofthe plurality of dialogues based on respective dialogue graphs of eachof the subset of the plurality of dialogues.
 8. The method of claim 7,wherein the determining the transitive relationships further comprisesdetermining a shortest path between a node representing an initialutterance and a node representing a resolution.
 9. The method of claim1, further comprising scoring user responses within the plurality ofdialogues, wherein the updating is further based on the scoring the userresponses.
 10. The method of claim 1, wherein a service provider atleast one of creates, maintains, deploys and supports the computingdevice.
 11. The method of claim 1, wherein the storing the plurality ofdialogues, the determining the transitive relationships, the updatingthe question mapping, and the applying the updated question map areprovided by a service provider on a subscription, advertising, and/orfee basis.
 12. The method of claim 1, wherein the computing deviceincludes software provided as a service in a cloud environment.
 13. Themethod of claim 1, further comprising deploying a system forstreamlining user support dialogues, comprising providing a computerinfrastructure operable to perform the storing the plurality ofdialogues, the determining the transitive relationships, the updatingthe question mapping, and the applying the updated question map.
 14. Acomputer program product for streamlining support dialogues, thecomputer program product comprising a computer readable non-transitorystorage medium having program instructions embodied therewith, theprogram instructions executable by a computing device to cause thecomputing device to: receive, via an automated support applicationhosted by the computing device, an initial user input from a user via auser device; provide one or more follow-up questions based on receivingthe initial user input and a question mapping; receive one or morefollow-up responses to the one or more follow-up questions; store adialogue, wherein the dialogue includes the initial user input, the oneor more follow-up questions, and the one or more follow-up responses;determine a transitive relationship between the stored dialogue and onemore previously stored dialogues; update the question mapping based onthe determining the transitive relationships; and apply the updatedquestion mapping to a subsequent support dialogue, wherein the programinstructions further cause the computing device to apply naturallanguage processing to automatically determine one or moreclassifications of the dialogue at different points in the dialogue, andthe storing the dialogue further includes storing the one or moreclassifications.
 15. The computer program product of claim 14, whereinthe different points in the dialogue comprise nodes in a dialogue graphassociated with the dialogue, the nodes representing responses by eachof the user device and the automated support application and a pathbetween the responses.
 16. The computer program product of claim 14,wherein the determining the transitive relationships includes comparingdialogue graphs associated with the stored dialogue and the one morepreviously stored dialogues.
 17. The computer program product of claim14, wherein the program instructions further cause the computing deviceto score user responses within the dialogues, wherein the updating isfurther based on the scoring the user responses.
 18. The computerprogram product of claim 14, wherein the program instructions furthercause the computing device to: abridge text transcripts associated withthe stored dialogue and the one more previously stored dialogues thatare transitively related; and apply the updated question mapping to thesubsequent support dialogue.
 19. A system comprising: a CPU, a computerreadable memory and a computer readable storage medium associated with acomputing device; program instructions to store a plurality of dialoguesbetween user devices and an automated support application hosted by thecomputing device; program instructions to cause the computing device toapply natural language processing to automatically determine one or moreclassifications of the dialogues at different points in the dialogues;program instructions to determine transitive relationships between theplurality of dialogues; program instructions to merge and abridge texttranscripts of a subset of the plurality of dialogues that aretransiently related to form an abridged text transcript; and programinstructions to apply the abridged text transcript to a subsequentsupport dialogue, wherein the storing the plurality of dialogues furtherincludes storing the one or more classifications, and the programinstructions are stored on the computer readable storage medium forexecution by the CPU via the computer readable memory.
 20. The system ofclaim 19, wherein the storing the plurality of dialogues comprisesprogram instructions to generate a dialogue graph of respective dialoggraphs associated with the plurality of dialogues, wherein thegenerating the dialogue graph comprises program instructions to generatenodes representing user responses and responses by the automated supportapplication and a path between the user responses the responses by theautomated support application.